Fast mode decomposition in few-mode fibers

Area of Science:

• Optics and Photonics
• Signal Processing
• Fiber Communications

Background:

• Optical phase information retrieval from intensity measurements is crucial for cost-efficient devices.
• Few-mode fibers (FMFs) offer potential for advanced applications, but require spatial mode structure knowledge for phase recovery.
• Current mode decomposition algorithms (optimization, neural networks) suffer from high computational costs and latency, hindering real-time applications.

Purpose of the Study:

• To present a high-performance, computationally efficient mode decomposition algorithm for FMFs.
• To overcome the speed limitations of existing phase retrieval techniques.
• To enable low-cost phase retrieval receivers for FMF applications.

Main Methods:

• Developed a novel mathematical algorithm for mode decomposition based on output intensity distribution.
• The algorithm does not employ machine learning techniques.
• Algorithm performance was evaluated based on processing time.

Main Results:

• Achieved a processing time in the tens of microseconds range.
• The proposed algorithm is orders of magnitude faster than state-of-the-art deep-learning methods.
• Demonstrated a significant improvement in signal processing speed for optical phase retrieval.

Conclusions:

• The developed algorithm offers a high-performance, low-latency solution for optical phase retrieval in FMFs.
• This non-machine learning approach can stimulate research beyond current deep-learning methods.
• Results pave the way for low-cost phase retrieval receivers in telecommunications and imaging.